Lung Ultrasound Signs: The Beginning. Part 3-An Accademia di Ecografia Toracica Comprehensive Review on Ultrasonographic Signs and Real Needs.

Over the last 20 years, scientific literature and interest on chest/lung ultrasound (LUS) have exponentially increased. Interpreting mixed-anatomical and artifactual-pictures determined the need of a proposal of a new nomenclature of artifacts and signs to simplify learning, spread, and implementation of this technique. The aim of this review is to collect and analyze different signs and artifacts reported in the history of chest ultrasound regarding normal lung, pleural pathologies, and lung consolidations. By reviewing the possible physical and anatomical interpretation of these artifacts and signs reported in the literature, this work aims to present the AdET (Accademia di Ecografia Toracica) proposal of nomenclature and to bring order between published studies.

A new 3D-printed temporal bone: 'the SAPIENS'-specific anatomical printed-3D-model in education and new surgical simulations.

PURPOSE: Otology and neuro-otology surgeries pose significant challenges due to the intricate and variable anatomy of the temporal bone (TB), requiring extensive training. In the last years 3D-printed temporal bone models for otological dissection are becoming increasingly popular. In this study, we presented a new 3D-printed temporal bone model named 'SAPIENS', tailored for educational and surgical simulation purposes. METHODS: The 'SAPIENS' model was a collaborative effort involving a multidisciplinary team, including radiologists, software engineers, ENT specialists, and 3D-printing experts. The development process spanned from June 2022 to October 2023 at the Department of Sense Organs, Sapienza University of Rome. Acquisition of human temporal bone images; temporal bone rendering; 3D-printing; post-printing phase; 3D-printed temporal bone model dissection and validation. RESULTS: The 'SAPIENS' 3D-printed temporal bone model demonstrated a high level of anatomical accuracy, resembling the human temporal bone in both middle and inner ear anatomy. The questionnaire-based assessment by five experienced ENT surgeons yielded an average total score of 49.4 ± 1.8 out of 61, indicating a model highly similar to the human TB for both anatomy and dissection. Specific areas of excellence included external contour, sigmoid sinus contour, cortical mastoidectomy simulation, and its utility as a surgical practice simulator. CONCLUSION: We have designed and developed a 3D model of the temporal bone that closely resembles the human temporal bone. This model enables the surgical dissection of the middle ear and mastoid with an excellent degree of similarity to the dissection performed on cadaveric temporal bones.

Human-to-AI Interrater Agreement for Lung Ultrasound Scoring in COVID-19 Patients.

OBJECTIVES: Lung ultrasound (LUS) has sparked significant interest during COVID-19. LUS is based on the detection and analysis of imaging patterns. Vertical artifacts and consolidations are some of the recognized patterns in COVID-19. However, the interrater reliability (IRR) of these findings has not been yet thoroughly investigated. The goal of this study is to assess IRR in LUS COVID-19 data and determine how many LUS videos and operators are required to obtain a reliable result. METHODS: A total of 1035 LUS videos from 59 COVID-19 patients were included. Videos were randomly selected from a dataset of 1807 videos and scored by six human operators (HOs). The videos were also analyzed by artificial intelligence (AI) algorithms. Fleiss' kappa coefficient results are presented, evaluated at both the video and prognostic levels. RESULTS: Findings show a stable agreement when evaluating a minimum of 500 videos. The statistical analysis illustrates that, at a video level, a Fleiss' kappa coefficient of 0.464 (95% confidence interval [CI] = 0.455-0.473) and 0.404 (95% CI = 0.396-0.412) is obtained for pairs of HOs and for AI versus HOs, respectively. At prognostic level, a Fleiss' kappa coefficient of 0.505 (95% CI = 0.448-0.562) and 0.506 (95% CI = 0.458-0.555) is obtained for pairs of HOs and for AI versus HOs, respectively. CONCLUSIONS: To examine IRR and obtain a reliable evaluation, a minimum of 500 videos are recommended. Moreover, the employed AI algorithms achieve results that are comparable with HOs. This research further provides a methodology that can be useful to benchmark future LUS studies.

Vertical Artifacts as Lung Ultrasound Signs: Trick or Trap? Part 2- An Accademia di Ecografia Toracica Position Paper on B-Lines and Sonographic Interstitial Syndrome.

Although during the last few years the lung ultrasound (LUS) technique has progressed substantially, several artifacts, which are currently observed in clinical practice, still need a solid explanation of the physical phenomena involved in their origin. This is particularly true for vertical artifacts, conventionally known as B-lines, and for their use in clinical practice. A wider consensus and a deeper understanding of the nature of these artifactual phenomena will lead to a better classification and a shared nomenclature, and, ultimately, result in a more objective correlation between anatomo-pathological data and clinical scenarios. The objective of this review is to collect and document the different signs and artifacts described in the history of chest ultrasound, with a particular focus on vertical artifacts (B-lines) and sonographic interstitial syndrome (SIS). By reviewing the possible physical and anatomical interpretation of the signs and artifacts proposed in the literature, this work also aims to bring order to the available studies and to present the AdET (Accademia di Ecografia Toracica) viewpoint in terms of nomenclature and clinical approach to the SIS.

New International Guidelines and Consensus on the Use of Lung Ultrasound.

Following the innovations and new discoveries of the last 10 years in the field of lung ultrasound (LUS), a multidisciplinary panel of international LUS experts from six countries and from different fields (clinical and technical) reviewed and updated the original international consensus for point-of-care LUS, dated 2012. As a result, a total of 20 statements have been produced. Each statement is complemented by guidelines and future developments proposals. The statements are furthermore classified based on their nature as technical (5), clinical (11), educational (3), and safety (1) statements.

Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees.

COVID-19 raised the need for automatic medical diagnosis, to increase the physicians' efficiency in managing the pandemic. Among all the techniques for evaluating the status of the lungs of a patient with COVID-19, lung ultrasound (LUS) offers several advantages: portability, cost-effectiveness, safety. Several works approached the automatic detection of LUS imaging patterns related COVID-19 by using deep neural networks (DNNs). However, the decision processes based on DNNs are not fully explainable, which generally results in a lack of trust from physicians. This, in turn, slows down the adoption of such systems. In this work, we use two previously built DNNs as feature extractors at the frame level, and automatically synthesize, by means of an evolutionary algorithm, a decision tree (DT) that aggregates in an interpretable way the predictions made by the DNNs, returning the severity of the patients' conditions according to a LUS score of prognostic value. Our results show that our approach performs comparably or better than previously reported aggregation techniques based on an empiric combination of frame-level predictions made by DNNs. Furthermore, when we analyze the evolved DTs, we discover properties about the DNNs used as feature extractors. We make our data publicly available for further development and reproducibility.

Lung Ultrasound in COVID-19 and Post-COVID-19 Patients, an Evidence-Based Approach.

OBJECTIVES: Worldwide, lung ultrasound (LUS) was utilized to assess coronavirus disease 2019 (COVID-19) patients. Often, imaging protocols were however defined arbitrarily and not following an evidence-based approach. Moreover, extensive studies on LUS in post-COVID-19 patients are currently lacking. This study analyses the impact of different LUS imaging protocols on the evaluation of COVID-19 and post-COVID-19 LUS data. METHODS: LUS data from 220 patients were collected, 100 COVID-19 positive and 120 post-COVID-19. A validated and standardized imaging protocol based on 14 scanning areas and a 4-level scoring system was implemented. We utilized this dataset to compare the capability of 5 imaging protocols, respectively based on 4, 8, 10, 12, and 14 scanning areas, to intercept the most important LUS findings. This to evaluate the optimal trade-off between a time-efficient imaging protocol and an accurate LUS examination. We also performed a longitudinal study, aimed at investigating how to eventually simplify the protocol during follow-up. Additionally, we present results on the agreement between AI models and LUS experts with respect to LUS data evaluation. RESULTS: A 12-areas protocol emerges as the optimal trade-off, for both COVID-19 and post-COVID-19 patients. For what concerns follow-up studies, it appears not to be possible to reduce the number of scanning areas. Finally, COVID-19 and post-COVID-19 LUS data seem to show differences capable to confuse AI models that were not trained on post-COVID-19 data, supporting the hypothesis of the existence of LUS patterns specific to post-COVID-19 patients. CONCLUSIONS: A 12-areas acquisition protocol is recommended for both COVID-19 and post-COVID-19 patients, also during follow-up.

Barbed reposition pharyngoplasty (BRP) in obstructive sleep apnea treatment: State of the art.

PURPOSE: In this paper, we perform a systematic review that discusses the state of the art and evolution on the barbed reposition pharyngoplasty (BRP) in the velo-pharyngeal surgery. Clinical evidence and published outcomes of this surgical technique are reported and discussed. MATERIALS AND METHODS: We performed a systematic review of the current literature through the analysis of the last 10 years of literature on barbed palate surgery. Study design, number of patients enrolled, inclusion criteria, pre- and posttreatment outcomes (AHI, ODI), surgical success rate, follow-up time and complication has been collected and reported. RESULTS: 15 studies for a total of 1531 patients, out of which 1061 underwent barbed reposition pharyngoplasty. Five trials were uncontrolled prospective studies (215 patients, 14% of total), nine were retrospective studies (1266 patients, 82,6% of total), and one randomized prospective clinical trial (RCT) (50 patients, 3,32% of total). All analyzed studies reported good outcomes after BRP surgery. Average preoperative values of AHI and ODI reduced in all studies considered with a significative statistical difference between preoperative and postoperative values (p < 0.05 in all cases). The postoperative surgical success rate ranged between 65.4 and 93% of cases. There were no significant intra-operative or post-operative complications in all studies considered in this systematic review. CONCLUSIONS: Barbed reposition pharyngoplasty has proven to be an easy to learn, quick, safe and effective new palatopharyngeal procedure, that can be used in a single level surgery or as a part of multilevel procedures.

Impact of COVID-19 pandemic on the incidence of otitis media with effusion in adults and children: a multicenter study.

PURPOSE: To compare and analyze the incidence of otitis media with effusion (OME), before and during the COVID-19-related pandemic period, to evaluate the effects of the social changes (lockdown, continuous use of facial masks, social distancing, reduction of social activities) in the OME incidence in children and adults. METHODS: The number of diagnosed OME in e five referral centers, between 1 March 2018 and 1 March 2021, has been reviewed and collected. To estimate the reduction of OME incidence in children and adults during the COVID-19 pandemic period the OME incidence in three period of time were evaluated and compared: group 1-patients with OME diagnosis achieved between 1/03/2018 and 01/03/2019 (not pandemic period). Group 2-patients with OME diagnosis achieved between 1/03/2019 and 1/03/2020 (not pandemic period). Group 3-patients with OME diagnosis achieved between 1/03/2020 and 1/03/2021 (COVID-19 pandemic period). RESULTS: In the non-pandemic periods (group 1 and 2), the incidence of OME in the five referral centers considered was similar, with 482 and 555 diagnosed cases, respectively. In contrast, the OME incidence in the same centers, during the pandemic period (group 3) was clearly reduced with a lower total number of 177 cases of OME estimated. Percentage variation in OME incidence between the first non-pandemic year considered (group 1) and the pandemic period (group 3) was-63, 3%, with an absolute value decrease value of-305 cases. Similarly, comparing the second non-pandemic year (group 2) and the pandemic year (group 3) the percentage variation of OME incidence was-68, 1% with an absolute value of-305 cases decreased. CONCLUSIONS: Our findings showed a lower incidence of OME during the pandemic period compared with 2 previous non pandemic years. The drastic restrictive anti-contagion measures taken by the Italian government to contain the spread of COVID-19 could have had a positive impact on the lower OME incidence during the last pandemic year.

On the Impact of Different Lung Ultrasound Imaging Protocols in the Evaluation of Patients Affected by Coronavirus Disease 2019: How Many Acquisitions Are Needed?

Lung ultrasound (LUS) is currently being extensively used for the evaluation of patients affected by coronavirus disease 2019. In the past months, several imaging protocols have been proposed in the literature. However, how the different protocols would compare when applied to the same patients had not been investigated yet. To this end, in this multicenter study, we analyzed the outcomes of 4 different LUS imaging protocols, respectively based on 4, 8, 12, and 14 LUS acquisitions, on data from 88 patients. Results show how a 12-area acquisition system seems to be a good tradeoff between the acquisition time and accuracy.

A New Lung Ultrasound Protocol Able to Predict Worsening in Patients Affected by Severe Acute Respiratory Syndrome Coronavirus 2 Pneumonia.

OBJECTIVES: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can generate severe pneumonia associated with high mortality. A bedside lung ultrasound (LUS) examination has been shown to have a potential role in this setting. The purpose of this study was to evaluate the potential prognostic value of a new LUS protocol (evaluation of 14 anatomic landmarks, with graded scores of 0-3) in patients with SARS-CoV-2 pneumonia and the association of LUS patterns with clinical or laboratory findings. METHODS: A cohort of 52 consecutive patients with laboratory-confirmed SARS-CoV-2 underwent LUS examinations on admission in an internal medicine ward and before their discharge. A total LUS score as the sum of the scores at each explored area was computed. We investigated the association between the LUS score and clinical worsening, defined as a combination of high-flow oxygen support, intensive care unit admission, or 30-day mortality as the primary end point. RESULTS: Twenty (39%) patients showed a worse outcome during the observation period; the mean LUS scores ± SDs were 20.4 ± 8.5 and 29.2 ± 7.3 in patients without and with worsening, respectively (P < .001). In a multivariable analysis, adjusted for comorbidities (>2), age (>65 years), sex (male), and body mass index (≥25 kg/m2 ), the association between the LUS score and worsening (odds ratio, 1.17; 95% confidence interval, 1.05 to 1.29; P = .003) was confirmed, with good discrimination of the model (area under the receiver operating characteristic curve, 0.82). A median LUS score higher than 24 was associated with an almost 6-fold increase in the odds of worsening (odds ratio, 5.67; 95% confidence interval, 1.29 to 24.8; P = .021). CONCLUSIONS: Lung ultrasound can represent an effective tool for monitoring and stratifying the prognosis of patients with SARS-CoV-2 pulmonary involvement.

Deep learning applied to lung ultrasound videos for scoring COVID-19 patients: A multicenter study.

In the current pandemic, lung ultrasound (LUS) played a useful role in evaluating patients affected by COVID-19. However, LUS remains limited to the visual inspection of ultrasound data, thus negatively affecting the reliability and reproducibility of the findings. Moreover, many different imaging protocols have been proposed, most of which lacked proper clinical validation. To address these problems, we were the first to propose a standardized imaging protocol and scoring system. Next, we developed the first deep learning (DL) algorithms capable of evaluating LUS videos providing, for each video-frame, the score as well as semantic segmentation. Moreover, we have analyzed the impact of different imaging protocols and demonstrated the prognostic value of our approach. In this work, we report on the level of agreement between the DL and LUS experts, when evaluating LUS data. The results show a percentage of agreement between DL and LUS experts of 85.96% in the stratification between patients at high risk of clinical worsening and patients at low risk. These encouraging results demonstrate the potential of DL models for the automatic scoring of LUS data, when applied to high quality data acquired accordingly to a standardized imaging protocol.

Investigating training-test data splitting strategies for automated segmentation and scoring of COVID-19 lung ultrasound images.

Ultrasound in point-of-care lung assessment is becoming increasingly relevant. This is further reinforced in the context of the COVID-19 pandemic, where rapid decisions on the lung state must be made for staging and monitoring purposes. The lung structural changes due to severe COVID-19 modify the way ultrasound propagates in the parenchyma. This is reflected by changes in the appearance of the lung ultrasound images. In abnormal lungs, vertical artifacts known as B-lines appear and can evolve into white lung patterns in the more severe cases. Currently, these artifacts are assessed by trained physicians, and the diagnosis is qualitative and operator dependent. In this article, an automatic segmentation method using a convolutional neural network is proposed to automatically stage the progression of the disease. 1863 B-mode images from 203 videos obtained from 14 asymptomatic individual,14 confirmed COVID-19 cases, and 4 suspected COVID-19 cases were used. Signs of lung damage, such as the presence and extent of B-lines and white lung areas, are manually segmented and scored from zero to three (most severe). These manually scored images are considered as ground truth. Different test-training strategies are evaluated in this study. The results shed light on the efficient approaches and common challenges associated with automatic segmentation methods.

Proposal for International Standardization of the Use of Lung Ultrasound for Patients With COVID-19: A Simple, Quantitative, Reproducible Method.

Growing evidence is showing the usefulness of lung ultrasound in patients with the 2019 new coronavirus disease (COVID-19). Severe acute respiratory syndrome coronavirus 2 has now spread in almost every country in the world. In this study, we share our experience and propose a standardized approach to optimize the use of lung ultrasound in patients with COVID-19. We focus on equipment, procedure, classification, and data sharing.